Serum Metabolites Associated with Blood Pressure in Chronic Kidney Disease Patients

Abstract

Blood pressure is one of the most basic health screenings and it has a complex relationship with chronic kidney disease (CKD). Controlling blood pressure for CKD patients is crucial for curbing kidney function decline and reducing the risk of cardiovascular disease. Two independent CKD cohorts, including matched controls (discovery n = 824; validation n = 552), were recruited. High-throughput metabolomics was conducted with the patients' serum samples using mass spectrometry. After controlling for CKD severity and other clinical hypertension risk factors, we identified ten metabolites that have significant associations with blood pressure. The quantitative importance of these metabolites was verified in a fully connected neural network model. Of the ten metabolites, seven have not previously been associated with blood pressure. The metabolites that had the strongest positive association with blood pressure were aspartylglycosamine (p = 4.58 × 10^-5), fructose-1,6-diphosphate (p = 1.19 × 10^-4) and N-Acetylserine (p = 3.27 × 10^-4). Three metabolites that were negatively associated with blood pressure (phosphocreatine, p = 6.39 × 10^-3; dodecanedioic acid, p = 0.01; phosphate, p = 0.04) have been reported previously to have beneficial effects on hypertension. These results suggest that intake of metabolites as supplements may help to control blood pressure in CKD patients.

Keywords: blood pressure; chronic kidney disease; hypertension.

Figure 1

The final ten metabolites that were associated with blood pressure. (A). The box plots of the final ten metabolites’ abundances by blood pressure stages (1–4): 1 indicates normal blood pressure, 2 indicates prehypertension, 3 indicates hypertension, 4 indicates crisis. (B). The ROC curves of the ten metabolites when treating blood pressure as binary (normal vs. high).

Figure 2

Additional validation of the importance of the final ten metabolites. (A) R2 and deep learning model’s accuracy increased as more metabolite was added. (B) The fully connected neural network model architecture.